EP 4242934 A1 20230913 - QUANTUM-KERNEL-BASED REGRESSION
Title (en)
QUANTUM-KERNEL-BASED REGRESSION
Title (de)
QUANTUM-KERNEL-BASIERTE REGRESSION
Title (fr)
RÉGRESSION BASÉE SUR LE NOYAU QUANTIQUE
Publication
Application
Priority
EP 22160629 A 20220307
Abstract (en)
Methods and systems are disclosed for solving a regression problem, for example a data regression problem and/or a differential equation problem, over a problem domain using a hybrid computer system. The hybrid computer system comprises a quantum computer system and a classical computer system. The method comprises receiving or determining, by the classical computer system, a regression problem description and a set of kernel points in the problem domain. The method may further comprise receiving or determining, by the classical computer system, a trial function associated with the regression problem, the trial function being based on a quantum kernel and being parameterized by one or more kernel coefficients. The quantum kernel may be based on an overlap of two wave functions. The method further comprises determining, using the quantum computer system, for each of the kernel points, a kernel value of the quantum kernel and/or a kernel derivative value of a derivative of the quantum kernel. The method further comprises determining, by the classical computer system, a set of optimal kernel coefficients based on the kernel value and/or kernel derivative value and determining, by the classical computer system, a solution function based on the trial function and the set of optimal kernel coefficients.
IPC 8 full level
G06N 10/40 (2022.01); G06N 10/60 (2022.01); G06N 20/10 (2019.01)
CPC (source: EP)
G06N 10/60 (2022.01); G06N 20/10 (2019.01); G06N 10/20 (2022.01); G06N 10/40 (2022.01)
Citation (applicant)
- US 2020394550 A1 20201217 - FUJII KEISUKE [JP], et al
- EP 2021081737 W 20211115
- EP 21190216 A 20210806
- EP 22155513 A 20220207
- H. BUHRMAN ET AL.: "Quantum fingerprinting", PHYSICAL REVIEW LETTERS, vol. 87, no. 16, 2001, pages 167902
Citation (search report)
- [XI] US 2020320437 A1 20201008 - GAMBETTA JAY M [US], et al
- [A] VOJTECH HAVLICEK ET AL: "Supervised learning with quantum enhanced feature spaces", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 30 April 2018 (2018-04-30), XP081174873, DOI: 10.1038/S41586-019-0980-2
- [A] MARIA SCHULD: "Supervised quantum machine learning models are kernel methods", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 17 April 2021 (2021-04-17), XP081928483
- [A] MARCELLO BENEDETTI ET AL: "Parameterized quantum circuits as machine learning models", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 18 June 2019 (2019-06-18), XP081383181
Designated contracting state (EPC)
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated extension state (EPC)
BA ME
Designated validation state (EPC)
KH MA MD TN
DOCDB simple family (publication)
DOCDB simple family (application)
EP 22160629 A 20220307; EP 2023055620 W 20230306